# Copyright 2017-2022 John Snow Labs
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from sparknlp.common import *
from sparknlp.annotator.classifier_dl import BertForQuestionAnswering
[docs]class TapasForQuestionAnswering(BertForQuestionAnswering):
"""TapasForQuestionAnswering is an implementation of TaPas - a BERT-based model specifically designed for
answering questions about tabular data. It takes TABLE and DOCUMENT annotations as input and tries to answer
the questions in the document by using the data from the table. The model is based in BertForQuestionAnswering
and shares all its parameters with it.
Pretrained models can be loaded with :meth:`.pretrained` of the companion
object:
>>> tapas = TapasForQuestionAnswering.pretrained() \\
... .setInputCols(["table", "document"]) \\
... .setOutputCol("answer")
The default model is ``"table_qa_tapas_base_finetuned_wtq"``, if no name
is provided.
For available pretrained models please see the `Models Hub
<https://sparknlp.org/models?task=Question+Answering+Tapas>`__.
====================== ======================
Input Annotation types Output Annotation type
====================== ======================
``DOCUMENT, TABLE`` ``CHUNK``
====================== ======================
Parameters
----------
batchSize
Batch size. Large values allows faster processing but requires more
memory, by default 2
caseSensitive
Whether to ignore case in tokens for embeddings matching, by default
False
configProtoBytes
ConfigProto from tensorflow, serialized into byte array.
maxSentenceLength
Max sentence length to process, by default 512
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>>
>>> document_assembler = MultiDocumentAssembler()\\
... .setInputCols("table_json", "questions")\\
... .setOutputCols("document_table", "document_questions")
>>>
>>> sentence_detector = SentenceDetector()\\
... .setInputCols(["document_questions"])\\
... .setOutputCol("questions")
>>>
>>> table_assembler = TableAssembler()\\
... .setInputCols(["document_table"])\\
... .setOutputCol("table")
>>>
>>> tapas = TapasForQuestionAnswering\\
... .pretrained()\\
... .setInputCols(["questions", "table"])\\
... .setOutputCol("answers")
>>>
>>> pipeline = Pipeline(stages=[
... document_assembler,
... sentence_detector,
... table_assembler,
... tapas])
>>>
>>> json_data = \"\"\"
... {
... "header": ["name", "money", "age"],
... "rows": [
... ["Donald Trump", "$100,000,000", "75"],
... ["Elon Musk", "$20,000,000,000,000", "55"]
... ]
... }
... \"\"\"
>>> model = pipeline.fit(data)
>>> model\\
... .transform(data)\\
... .selectExpr("explode(answers) AS answer")\\
... .select("answer.metadata.question", "answer.result")\\
... .show(truncate=False)
+-----------------------+----------------------------------------+
|question |result |
+-----------------------+----------------------------------------+
|Who earns 100,000,000? |Donald Trump |
|Who has more money? |Elon Musk |
|How much they all earn?|COUNT($100,000,000, $20,000,000,000,000)|
|How old are they? |AVERAGE(75, 55) |
+-----------------------+----------------------------------------+
"""
name = "TapasForQuestionAnswering"
inputAnnotatorTypes = [AnnotatorType.TABLE, AnnotatorType.DOCUMENT]
@keyword_only
def __init__(self, classname="com.johnsnowlabs.nlp.annotators.classifier.dl.TapasForQuestionAnswering",
java_model=None):
super(TapasForQuestionAnswering, self).__init__(
classname=classname,
java_model=java_model
)
self._setDefault(
batchSize=2,
maxSentenceLength=512,
caseSensitive=False
)
@staticmethod
[docs] def loadSavedModel(folder, spark_session):
"""Loads a locally saved model.
Parameters
----------
folder : str
Folder of the saved model
spark_session : pyspark.sql.SparkSession
The current SparkSession
Returns
-------
TapasForQuestionAnswering
The restored model
"""
from sparknlp.internal import _TapasForQuestionAnsweringLoader
jModel = _TapasForQuestionAnsweringLoader(folder, spark_session._jsparkSession)._java_obj
return TapasForQuestionAnswering(java_model=jModel)
@staticmethod
[docs] def pretrained(name="table_qa_tapas_base_finetuned_wtq", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default
"table_qa_tapas_base_finetuned_wtq"
lang : str, optional
Language of the pretrained model, by default "en"
remote_loc : str, optional
Optional remote address of the resource, by default None. Will use
Spark NLPs repositories otherwise.
Returns
-------
TapasForQuestionAnswering
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(TapasForQuestionAnswering, name, lang, remote_loc)